docs: def ramp_function(data_type, attr, fname, xinstfeature, xNNifeature):
      """ Our own user simplified variation of the ramp function suggested by Hong 1994, 1997. Hong's method requires the user to specifiy two thresholds
      that indicate the max difference before a score of 1 is given, as well a min difference before a score of 0 is given, and any in the middle get a
      score that is the normalized difference between the two continuous feature values. This was done because when discrete and continuous features were mixed,
      continuous feature scores were underestimated.  Towards simplicity, automation, and a dataset adaptable approach,
      here we simply check whether the difference is greater than the standard deviation for the given feature; if so we assign a score of 1, otherwise we
      assign the normalized feature score difference.  This should help compensate for the underestimation. """
      diff = 0
      mmdiff = attr[fname][3]  # Max/Min range of values for target feature
      rawfd = abs(xinstfeature - xNNifeature)  # prenormalized feature value difference
  
      if data_type == 'mixed':  # Ramp function utilized
          # Check whether feature value difference is greater than the standard deviation
          standDev = attr[fname][4]
          if rawfd > standDev:  # feature value difference is is wider than a standard deviation
              diff = 1
          else:
              diff = abs(xinstfeature - xNNifeature) / mmdiff
  
      else:  # Normal continuous feature scoring
          diff = abs(xinstfeature - xNNifeature) / mmdiff
  
      return diff